TY - JOUR

T1 - Spatial Gated Multi-Layer Perceptron for Land Use and Land Cover Mapping

AU - Jamali, Ali

AU - Roy, Swalpa Kumar

AU - Hong, Danfeng

AU - Atkinson, Peter M

AU - Ghamisi, Pedram

PY - 2024/1/15

Y1 - 2024/1/15

N2 - Due to its capacity to recognize detailed spectral differences, hyperspectral data have been extensively used for precise Land Use Land Cover (LULC) mapping. However, recent multi-modal methods have shown their superior classification performance over the algorithms that use single data sets. On the other hand, Convolutional Neural Networks (CNNs) are models extensively utilized for the hierarchical extraction of features. Vision transformers (ViTs), through a self-attention mechanism, have recently achieved superior modeling of global contextual information compared to CNNs. However, to harness their image classification strength, ViTs require substantial training datasets. In cases where the available training data is limited, current advanced multi-layer perceptrons (MLPs) can provide viable alternatives to both deep CNNs and ViTs. In this paper, we developed the SGU-MLP, a deep learning algorithm that effectively combines MLPs and spatial gating units (SGUs) for precise Land Use Land Cover (LULC) mapping using multi-modal data from multi-spectral, LiDAR, and hyperspectral data. Results illustrated the superiority of the developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based models, including HybridSN, ResNet, iFormer, EfficientFormer, and CoAtNet. The SGU-MLP classification model consistently outperformed the benchmark CNN and CNN-ViT-based algorithms. The code will be made publicly available at https: //github.com/aj1365/SGUMLP.

AB - Due to its capacity to recognize detailed spectral differences, hyperspectral data have been extensively used for precise Land Use Land Cover (LULC) mapping. However, recent multi-modal methods have shown their superior classification performance over the algorithms that use single data sets. On the other hand, Convolutional Neural Networks (CNNs) are models extensively utilized for the hierarchical extraction of features. Vision transformers (ViTs), through a self-attention mechanism, have recently achieved superior modeling of global contextual information compared to CNNs. However, to harness their image classification strength, ViTs require substantial training datasets. In cases where the available training data is limited, current advanced multi-layer perceptrons (MLPs) can provide viable alternatives to both deep CNNs and ViTs. In this paper, we developed the SGU-MLP, a deep learning algorithm that effectively combines MLPs and spatial gating units (SGUs) for precise Land Use Land Cover (LULC) mapping using multi-modal data from multi-spectral, LiDAR, and hyperspectral data. Results illustrated the superiority of the developed SGU-MLP classification algorithm over several CNN and CNN-ViT-based models, including HybridSN, ResNet, iFormer, EfficientFormer, and CoAtNet. The SGU-MLP classification model consistently outperformed the benchmark CNN and CNN-ViT-based algorithms. The code will be made publicly available at https: //github.com/aj1365/SGUMLP.

KW - Electrical and Electronic Engineering

KW - Geotechnical Engineering and Engineering Geology

U2 - 10.1109/lgrs.2024.3354175

DO - 10.1109/lgrs.2024.3354175

M3 - Journal article

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

SN - 1545-598X

ER -